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If the state estimation error is to converge to zero, then eqn (367) must be asymptotically stable Eqn (367) will be asymptotically (actually exponentially) stable if the eigenvalues of ( LH) have magnitude less than one The above analysis is meant to motivate the idea of state estimation (also called state observation) Several questions remain unanswered The above analysis has tacitly assumed that it is possible to choose the matrix L so that the eigenvalues of ( LH) have magnitude less than one This may not always be possible This issue is related to the question of when the system state can be estimated from a given set of measurements This issue is discussed in Section 361 Once it is determined that state estimation is possible for a given system, it is natural to consider whether it is possible to derive an optimal state estimator relative to a given optimality criteria The above analysis also only discusses time-invariant systems The timevarying case is more complex and will only be discussed in the context of optimal estimation Note that the resulting state estimation algorithm is recursive in nature and can be split into two parts: a measurement update and a time k update At k-th time step prior to using yk , we have x , yk , and yk The computation for the k-th time step proceeds as follows: yk k Posterior state estimate: x+ A priori state estimate: xk+1 A priori output estimate: yk+1 Residual: = = = = yk yk k x + L yk + + uk xk H xk+1 (368) (369) (370) (371)

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where L = 1 L This ordering of the operations is depicted in Figure 35 Eqn (368) computes the measurement residual Eqn (369) can be thought of as a blending of all past measurement information propagated to k the present time (as represented by x ) with the new information available from the current measurement (as represented by the residual yk ) While k x represents the estimate of xk using all measurements up to but not k including yk , the vector x+ represents the estimate of xk after incorporating all measurements including yk Performing the operations in this order minimizes the computation (ie, delay) between the arrival of yk and the k availability of x+ for use in control (eg, calculation of uk ), navigation, and planning computations After those computations are complete, but prior to the arrival of the next measurement, eqns (370-371) perform the time

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Figure 35: State estimator implementation organized to minimize delay between arrival of yk and computation of xk

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